A Set Space Model for Feature Calculus
Yanping Chen, Qinghua Zheng, Ping Chen · IEEE Intelligent Systems · 2017
Processing natural language at the sentence level suffers from a sparse-feature problem caused by the limited number of words in a sentence. In this article, a Set Space Model (SSM) is proposed to utilize sentence information, the main idea being that, depending on structural characteristics or functional principles of linguistics, features in a sentence can be grouped into different sets. Feature calculus can then operate on the grouped features and capture structural information using external knowledge. The authors implement this method in a traditional information extraction task, with results showing significant and constant improvement in general information extraction.